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Design Factory New Zealand: Data Analysis

Online tools

Dragon Transcribing App

The most accurate professional-grade dictation service available on the market. Create templates, add custom words, and instantly dictate your documents – Dragon Anywhere will automatically adapt to how you speak. Download your one-week FREE TRIAL now! Trial converts to a monthly ($14.99) or annual ($149.99) subscription.

NVivio

NVivo 11: This is fairly easy and intuitive to use. This has a wide range of features such as the options to integrate with social media, Evernote (one place to store ideas), EndNote (reference manager), SurveyMonkey, and OneNote. Not only can you use this software for qualitative analysis but doing a literature review. 

Qualtrics XM

Our software gives you the tools to ask the right questions, listen to what customers need, and respond with the right actions, every time. We call it empathy at scale — others just call it good business. - this is a paid tool

Excel

Data Analysis

Note. From "Decide your data analysis" [Video], by Scribbr, 2019, May 29. YouTube. (https://youtu.be/rNulPTwLFMQ). Copyright 2021, by Scribbr. Reprinted with permission (expiry date 3/03/24).

The data analysis section is where you report the main findings of the data collection and analysis you conducted for your thesis or dissertation. You should write in such a way that you only include what is relevant as logically, concisely, and objectively as you can. Don’t include subjective interpretations unless you have combined these sections together. 

Quantitative Data Analysis

It is more likely that you will have Qualitative Data in this area of study but you might do mixed methods. Which is both Qualitative and Quantitative data. 

  • Tables are used to communicate exact values, giving a concise overview of various results
  • Graphs and charts are used to visualize trends and relationships, giving an at-a-glance illustration of key findings

Qualitative Data Analysis and Discussion

Qualitative data is typically generated through:

  • Interview transcripts
  • Surveys with open-ended questions
  • Contact center transcripts
  • Texts and documents
  • Audio and video recordings
  • Observational notes

Compared to quantitative data, which captures structured information, qualitative data is unstructured and has more depth. It can answer our questions, can help formulate hypotheses and build understanding.

Qualitative data analysis is a process of organizing and interpreting the collected data in a logical way. Qualitative data is non-numerical and can be open-ended. Qualitative data refers to written responses, such as open-ended answers to survey questions or user interviews. It can also include audio, photos, and video files.

Common approaches to Qualitative Data

Narrative Analysis

As in its name, a narrative analysis focuses on the stories that are told by your stakeholders and the language and words they use to make sense of them.  It is particularly useful for getting a deep understanding of stakeholders' perspectives on your wicked problem. A narrative analysis could be useful if you have chosen case studies as your method of gathering information.

Discourse Analysis

Discourse analysis can be used to get a deeper understanding of the political, cultural, and power dynamics that exist around a wicked problem. This could relate directly to the two TDR axioms. The ontological axiom and the complexity axiom.  Discourse analysis could be used to understand why a group of people feel the way they do about a situation.

Thematic Analysis

Thematic analysis is used to deduce the meaning behind the words people use. This is accomplished by discovering repeating codes, themes, and concepts in direct quotes. These meaningful codes, themes, and concepts reveal key insights into data and can be quantified. So the process of thematic analysis is also referred to as “coding”. The software Miro is great for doing this type of analysis. 

Grounded Theory

Grounded theory is a useful approach when little is known about a subject. Grounded theory starts by formulating a theory around a single data case. This means that the theory is “grounded”. It’s based on actual data, and not entirely speculative. Then additional cases can be examined to see if they are relevant and can add to the original theory.

A starting point

Since your qualitative research will be directly related to your research questions, you can write your results under the key themes or topics that emerged from your data.

General observations of the results from your data are a great place to start. 

  • Differing viewpoints - This feeds nicely into the TDR framework and discovering the included middle under the logical axiom. 
  • Patterns and trends
  • Particularly significant snippets from individual responses

Then, clarify and support these points with direct quotations. Your transcripts and analysis tools will be included in your appendix or your portfolio of evidence or both.

The 4 steps to doing qualitative data analysis

  1. Gathering and collecting your qualitative data
     
  2. Organizing your qualitative data
     
  3. Coding your qualitative data
     
  4. Analyzing the qualitative data for insights

Organizing your data

If all data is easily accessible in one place and analyzed in a consistent manner, it will be easier to summarize and gather insights and codes from the data.

The manual way of organizing your data is to put it into a spreadsheet.

Miro Boards

You could also use a tool called Miro.

A miro board can arrange all your data in a visual way. A Miro board is great if you are doing Thematic Analysis but can also be used for other Supporting Methodologies. 

Coding your data

 After you organize your data, you can start coding it into themes and insights. At the bottom of this page are some online tools you may use for your data analysis. Coding means identifying keywords or phrases and assigning them to a category of meaning. Coding is the process of labeling and organizing your data into themes from your data, and the relationships between these themes. A simple way to do this is to do it in small steps.

  1. Think about the codes you could use. 
  2. Then assigns each piece of data a code. If you do this systematically patterns and themes will begin to emerge from your data.
  3. Once this is done, it is important to keep refining and revising your codes to make sure you have the greatest accuracy and rigor in your insights.

Finding insights

This is where we start answering our research questions and hypothesis. This part is time-consuming. Give yourself plenty of time to do this. Your job is to scour through the codes and discover insights and meanings from them. Make sure each insight is distinct and has data to support it. Maybe add some quotes to it. 

Talk to your supervisors about how to best show this in your thesis. 

George, T. (2022, November 11). How to Write a Results Section | Tips & Examples. Scribbr. Retrieved February 18, 2023, from https://www.scribbr.com/dissertation/results/

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